Fine-tuning digital FIR filters with gray wolf optimization for peak performance

Area of Science:

• Digital Signal Processing
• Computational Intelligence
• Filter Design

Background:

• Optimum filter design is crucial for signal processing, aiming for flat passbands and high stopband attenuation.
• Finite Impulse Response (FIR) filters are widely used, but their optimal design presents challenges.

Purpose of the Study:

• To solve the FIR filter design problem using nature-inspired optimization algorithms.
• To compare the effectiveness of Gray Wolf Optimization (GWO), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for FIR filter design.

Main Methods:

• Implementing GWO, CS, PSO, and GA to design FIR low-pass, high-pass, and band-stop filters.
• Evaluating filter performance based on stopband attenuation, passband ripples, and deviation from the desired response.
• Comparing algorithm execution times and the ability to achieve global optimal solutions.

Main Results:

• The Gray Wolf Optimization algorithm outperformed other methods in designing FIR filters.
• GWO resulted in enhanced design precision and reduced execution time compared to CS, PSO, and GA.
• All tested algorithms were capable of achieving optimal solutions for FIR filter design.

Conclusions:

• The Gray Wolf Optimization algorithm is a highly effective approach for designing digital FIR filters.
• GWO offers a superior balance of performance, precision, and efficiency in filter design applications.
• Nature-inspired algorithms provide robust solutions for complex filter design problems.